Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Karlberg (WO 2024/258602) in view of Lucas (US 2025/0190801).
With respect to claim 11 (similarly claims 1 and 20), Karlberg teaches an apparatus (e.g. a machine 600 Fig 6 [0069] to perform any one or more of the methodologies discussed), comprising:
one or more network interfaces to communicate with a network (e.g. network interface 620 Fig 6 to communicate with network 626 Fig 6 [0075]);
a processor (e.g. processor 602 Fig 6 [0073]) coupled to the one or more network interfaces and configured to execute one or more processes (e.g. execute instructions 624 Fig 6); and
a memory (e.g. a main memory 604 Fig 6 [0073]) configured to store a process that is executable by the processor (e.g. storing instructions 624 executable by processor 602), the process, when executed, configured to:
identify features of a prompt to be sent to a language model (e.g. identify features of a prompt to be sent to a language model as suggested in [0015]-[0016]) that are indicative of a type of data that the prompt would cause the language model to access (e.g. that are indicative of a type of data that the prompt would cause the language model to access as suggested in [0018]-[0019], see also [0091]);
determine whether data control policies apply for the prompt based on the type of data that the prompt would cause the language model to access (e.g. [0055], [0092]-[0093] determine whether data control policies apply for the prompt based on the type of data that the prompt would cause the language model to access);
Even though Karlberg teaches filtering the context data obtained from traversing the knowledge graph based on the access control policies for a user providing the query in [0093], he fails to teach determine, based on the features, whether processing of the prompt by the language model violates an applicable data control policy; and
prevent the language model from processing the prompt when the processing of the prompt violates the applicable data control policy.
Lucas teaches determine, based on the features, whether processing of a prompt by a language model violates an applicable data control policy (e.g. Based on the received probabilities, prompt analyzer 120 may determine if prompt 101 is a valid prompt or invalid prompt [0025] suggest to determine, based on the features, whether processing of a prompt by a language model violates an applicable data control policy, especially when the prompt is invalid); and
prevent the language model from processing the prompt when the processing of the prompt violates the applicable data control policy (e.g. Prompt 101 determined to be invalid may be returned to a (human or machine) user that generated prompt 101 with a suggestion to rephrase prompt 101 or a notification that prompt 101 cannot be processed [0025]).
Karlberg and Lucas are analogous art because they all pertain to processing prompts. Therefore, it would have been obvious to people having ordinary skill in the art before the effective filing date of the claimed invention to modify Karlberg with the prompt analyzer 120 of Lucas to include: determine, based on the features, whether processing of the prompt by the language model violates an applicable data control policy; and
prevent the language model from processing the prompt when the processing of the prompt violates the applicable data control policy, as suggested by Lucas in [0025]. The benefit of the modification would be to prevent processing a prompt whose content violates some relevant policy, a prompt that is likely to generate a response that would violate any relevant policy, as suggested in [0025] of Lucas.
With respect to claim 12 (similarly claim 2), Karlberg in view of Lucas teaches the apparatus as in claim 11, the process further configured to: identify the applicable data control policy by a similarity search of a repository of data control policy matrices for a data control policy that corresponds to the features of the prompt (Lucas e.g. [0031] suggest identify the applicable data control policy by a similarity search of a repository of data control policy matrices for a data control policy that corresponds to the features of the prompt).
With respect to claim 13 (similarly claim 3), Karlberg in view of Lucas teaches the apparatus as in claim 11, the process further configured to: identify the applicable data control policy by its correspondence to an identification associated with a submitter of the prompt (Lucas e.g. prompt augmentation 305 may include personality assigned to LM 124 by application 125, [0040] suggest identify the applicable data control policy by its correspondence to an identification associated with a submitter of the prompt).
With respect to claim 14 (similarly claim 4), Karlberg in view of Lucas teaches the apparatus as in claim 11, wherein preventing the language model from processing the prompt includes blocking the prompt from being provided to the language model for processing (Lucas e.g. Prompt 101 determined to be invalid may be returned to a (human or machine) user that generated prompt 101 with a suggestion to rephrase prompt 101 or a notification that prompt 101 cannot be processed [0025] suggest blocking the prompt from being provided to the language model for processing).
With respect to claim 15 (similarly claim 5), Karlberg in view of Lucas teaches the apparatus as in claim 11, wherein preventing the language model from processing the prompt includes filtering a portion of the prompt from being provided to the language model for processing (Karlberg e.g. filtering the context data obtained from traversing the knowledge graph based on the access control policies for a user providing the query [0093], claim 3 suggest filtering a portion of the prompt from being provided to the language model for processing).
With respect to claim 16 (similarly claim 6), Karlberg in view of Lucas teaches the apparatus as in claim 11, wherein preventing the language model from processing the prompt includes flagging the prompt for reengineering prior to being provided to the language model for processing (Lucas e.g. a notification that prompt 101 cannot be processed [0025] suggest flagging the prompt for reengineering prior to being provided to the language model for processing).
With respect to claim 17 (similarly claim 7), Karlberg in view of Lucas teaches the apparatus as in claim 11, the process further configured to: cause the prompt to be passed to the language model for processing responsive to a determination that processing of the prompt by the language model does not violate the applicable data control policy (Lucas e.g. Prompt 101 determined to be valid may be forwarded to LM 122 for regular processing [0025] suggest cause the prompt to be passed to the language model for processing responsive to a determination that processing of the prompt by the language model does not violate the applicable data control policy).
With respect to claim 18 (similarly claim 8), Karlberg in view of Lucas teaches the apparatus as in claim 17, wherein causing the prompt to be passed to the language model includes notifying a client service operating as an intermediate layer that the processing of the prompt by the language model does not violate the applicable data control policy (Lucas e.g. Prompt 101 determined to be valid may be forwarded to LM 122 for regular processing [0025] suggest wherein causing the prompt to be passed to the language model includes notifying a client service operating as an intermediate layer that the processing of the prompt by the language model does not violate the applicable data control policy).
With respect to claim 19 (similarly claim 9), Karlberg in view of Lucas teaches the apparatus as in claim 11, wherein control over the prompt is retained by an intermediate layer while determining whether the processing of the prompt by the language model violates the applicable data control policy (Lucas e.g. prompt analyzer 120 processing prompt 101 [0025] suggest wherein control over the prompt is retained by an intermediate layer while determining whether the processing of the prompt by the language model violates the applicable data control policy).
With respect to claim 10, Karlberg in view of Lucas teaches the method of claim 9, further comprising: parsing the prompt to automatically identify the features of the prompt, wherein the features of the prompt are further indicative of one or more of a task requested in the prompt, a constraint applicable to completing the task, or an expected output on completion of the task; and providing the features of the prompt as metadata associated with the prompt for comparison to data control policies (Lucas e.g. prompt analyzer 120 may parse prompt 101 into individual words or tokens and construct one or more verification prompts that include some of the words/tokens of prompt 101 while excluding some other words/tokens of prompt 101 Prompt analyzer 120 may feed the constructed verification prompts to LM 122 and receive, from LM 122 various probabilities (verification scores) indicating likelihoods that one or more words/tokens not included in verification prompts can occur together with words/tokens of verification prompts as part of the same prompt 101. Based on the received probabilities, prompt analyzer 120 may determine if prompt 101 is a valid prompt or invalid prompt. [0025] suggest the features of the prompt are further indicative of one or more of a task requested in the prompt, a constraint applicable to completing the task, or an expected output on completion of the task; and providing the features of the prompt as metadata associated with the prompt for comparison to data control policies).
Conclusion
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/IBRAHIM SIDDO/Primary Examiner, Art Unit 2681
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